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Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk

Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this...

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Autores principales: Ning, Ziyu, Feng, Chenchen, Song, Chao, Liu, Wei, Shang, Desi, Li, Meng, Wang, Qiuyu, Zhao, Jianmei, Liu, Yuejuan, Chen, Jiaxin, Yu, Xiaoyang, Zhang, Jian, Li, Chunquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6763789/
https://www.ncbi.nlm.nih.gov/pubmed/31408573
http://dx.doi.org/10.1002/1878-0261.12563
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author Ning, Ziyu
Feng, Chenchen
Song, Chao
Liu, Wei
Shang, Desi
Li, Meng
Wang, Qiuyu
Zhao, Jianmei
Liu, Yuejuan
Chen, Jiaxin
Yu, Xiaoyang
Zhang, Jian
Li, Chunquan
author_facet Ning, Ziyu
Feng, Chenchen
Song, Chao
Liu, Wei
Shang, Desi
Li, Meng
Wang, Qiuyu
Zhao, Jianmei
Liu, Yuejuan
Chen, Jiaxin
Yu, Xiaoyang
Zhang, Jian
Li, Chunquan
author_sort Ning, Ziyu
collection PubMed
description Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA‐mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA‐mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA‐mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway‐based, miRNA‐based, and gene‐based classification methods. The high‐frequency miRNA‐mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.
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spelling pubmed-67637892019-10-01 Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk Ning, Ziyu Feng, Chenchen Song, Chao Liu, Wei Shang, Desi Li, Meng Wang, Qiuyu Zhao, Jianmei Liu, Yuejuan Chen, Jiaxin Yu, Xiaoyang Zhang, Jian Li, Chunquan Mol Oncol Research Articles Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA‐mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA‐mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA‐mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway‐based, miRNA‐based, and gene‐based classification methods. The high‐frequency miRNA‐mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies. John Wiley and Sons Inc. 2019-08-27 2019-10 /pmc/articles/PMC6763789/ /pubmed/31408573 http://dx.doi.org/10.1002/1878-0261.12563 Text en © 2019 The Authors. Published by FEBS Press and John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Ning, Ziyu
Feng, Chenchen
Song, Chao
Liu, Wei
Shang, Desi
Li, Meng
Wang, Qiuyu
Zhao, Jianmei
Liu, Yuejuan
Chen, Jiaxin
Yu, Xiaoyang
Zhang, Jian
Li, Chunquan
Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk
title Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk
title_full Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk
title_fullStr Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk
title_full_unstemmed Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk
title_short Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk
title_sort topologically inferring active mirna‐mediated subpathways toward precise cancer classification by directed random walk
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6763789/
https://www.ncbi.nlm.nih.gov/pubmed/31408573
http://dx.doi.org/10.1002/1878-0261.12563
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